{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9ada8d8a",
   "metadata": {},
   "source": [
    "## jupyter notebook 扩展"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6c360e8",
   "metadata": {},
   "source": [
    "* https://zhuanlan.zhihu.com/p/97394628"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7444edb6",
   "metadata": {},
   "source": [
    "## jupyter notebook 键盘快捷键"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ba1e6dd",
   "metadata": {},
   "source": [
    "### 进入命令模式之后，可以尝试以下快捷键"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41b0634c",
   "metadata": {},
   "source": [
    "> 1. **A** 会在活跃单元之上插入一个新的单元  \n",
    "> 2. **B** 会在活跃单元之下插入一个新单元  \n",
    "> 3. 连续按**两次D**，可以删除一个单元  \n",
    "> 4. 撤销被删除的单元,按 **Z** \n",
    "> 5. **Y** 会将当前活跃的单元变成一个代码单元  \n",
    "> 6. 按住 **Shift +上或下箭头**可选择多个单元。在多选模式时，按住 **Shift + M** 可合并你的选择  \n",
    "> 7. 按 **F** 会弹出「查找和替换」菜单  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d170624",
   "metadata": {},
   "source": [
    "### 处于编辑模式时（在命令模式时按Enter），可以发现"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f488a22",
   "metadata": {},
   "source": [
    "> 1.**Ctrl + Home**到达单元起始位置  \n",
    "> 2. **Ctrl + S**保存进度  \n",
    "> 3. 如之前提到的,**Ctrl + Enter**会运行你的整个单元块  \n",
    "> 4. **Alt + Enter**不止会运行你的单元块，还会在下面添加一个新单元  \n",
    "> 5. **Ctrl + Shift + F**打开命令面板  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97fbe421",
   "metadata": {},
   "source": [
    "## 课程资源介绍"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd13b6f1",
   "metadata": {},
   "source": [
    "### [joyfulpandas]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "419ca1e9",
   "metadata": {},
   "source": [
    "> 1. [joyfulpandas](http://joyfulpandas.datawhale.club/)  \n",
    "> 2. [学习目录](https://gitee.com/link?target=http%3A%2F%2Fjoyfulpandas.datawhale.club%2FContent%2Findex.html)  \n",
    "> 3. [线下资源链接](https://gitee.com/link?target=http%3A%2F%2Fjoyfulpandas.datawhale.club%2Fpandas%25E6%2595%25B0%25E6%258D%25AE%25E5%25A4%2584%25E7%2590%2586%25E4%25B8%258E%25E5%2588%2586%25E6%259E%2590.html) \n",
    "> 4. [线上资源电子书](https://gitee.com/xzhichao/data_analysis/blob/master/%E5%AD%A6%E4%B9%A0%E8%B5%84%E6%BA%90/joyfulpandas.pdf)\n",
    "> 5. 代码资源  \n",
    ">> 1.[资源1](https://gitee.com/link?target=http%3A%2F%2Fjoyfulpandas.datawhale.club%2FContent%2Findex.html)   \n",
    ">> 2.[资源2·含data-source](https://gitee.com/link?target=https%3A%2F%2Fgithub.com%2Fdatawhalechina%2Fjoyful-pandas)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "746f0c57",
   "metadata": {},
   "source": [
    "### [panda]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34e152a1",
   "metadata": {},
   "source": [
    "> 1. [pandas chat-sheet查询表](https://gitee.com/link?target=https%3A%2F%2Fpandas.pydata.org%2FPandas_Cheat_Sheet.pdf)    \n",
    "> 2. [pandas Getting started](https://gitee.com/link?target=https%3A%2F%2Fpandas.pydata.org%2Fgetting_started.html)    \n",
    ">> 1. 环境搭建  \n",
    ">> 2. [Tutorials](https://gitee.com/link?target=https%3A%2F%2Fjupyterlab.readthedocs.io%2Fen%2Fstable%2Fuser%2Finterface.html)    \n",
    ">> 3. [Books](https://gitee.com/link?target=https%3A%2F%2Famzn.to%2F3DyLaJc)  \n",
    ">> 4. [Videos资源](https://gitee.com/link?target=https%3A%2F%2Fjupyterlab.readthedocs.io%2Fen%2Fstable%2Fuser%2Finterface.html)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32b807c4",
   "metadata": {},
   "source": [
    "### [JupyterLab]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f85b2244",
   "metadata": {},
   "source": [
    "> 1.focused on interactive  \n",
    "> 2.focused on exploratoy computing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3bf66f3",
   "metadata": {},
   "source": [
    "## Python 基础回顾（预备知识）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d7c250c",
   "metadata": {},
   "source": [
    "### 列表推导式与条件赋值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0927ec2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "student_index = [10001,10002,10003,10004,10005,10006]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9c75db5",
   "metadata": {},
   "source": [
    "* 需求： 学号 奇数先筛选出来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "94140cf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "奇数_students = []\n",
    "for i in student_index:\n",
    "    if i%2 !=0:\n",
    "        奇数_students.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "47a9d687",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10001, 10003, 10005]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "奇数_students"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7cc3960",
   "metadata": {},
   "source": [
    "* 列表推导式---->[结果变量 for循环 循环体]===>一切的循环都可以用推导式来写"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1933edc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10001, 10003, 10005]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "奇数_students = [i for i in student_index if i%2!=0]\n",
    "奇数_students"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebed389d",
   "metadata": {},
   "source": [
    "* 课本的基础"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "45150c43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a_c', 'a_d', 'b_c', 'b_d']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = [m+'_'+n for m in ['a','b'] for n in ['c','d']]\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "85916c30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = [m for m in ['a','b']]\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ba7aa5cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'a', 'b', 'b']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = [m for m in ['a','b'] for n in ['c','d']]\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29d02fd4",
   "metadata": {},
   "source": [
    "* 以for循环呈现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "389a8c3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "for m in ['a','b']:\n",
    "    for n in ['c','d']:\n",
    "        results.append(m+'_'+n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c0718e0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a_c', 'a_d', 'b_c', 'b_d']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b92ebe9c",
   "metadata": {},
   "source": [
    "### 文件的读取和写入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d8562d19",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c25cfbfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>企业</td>\n",
       "      <td>价值（亿元人民币）</td>\n",
       "      <td>国家</td>\n",
       "      <td>城市</td>\n",
       "      <td>行业</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>币安</td>\n",
       "      <td>3000</td>\n",
       "      <td>马耳他</td>\n",
       "      <td>马耳他</td>\n",
       "      <td>区块链</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Citadel Securities</td>\n",
       "      <td>1500</td>\n",
       "      <td>美国</td>\n",
       "      <td>芝加哥</td>\n",
       "      <td>金融科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>极兔速递</td>\n",
       "      <td>1300</td>\n",
       "      <td>印度尼西亚</td>\n",
       "      <td>雅加达</td>\n",
       "      <td>电子商务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>极星</td>\n",
       "      <td>1300</td>\n",
       "      <td>瑞典</td>\n",
       "      <td>哥德堡</td>\n",
       "      <td>新能源汽车</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Notion</td>\n",
       "      <td>670</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>软件服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0</td>\n",
       "      <td>Airtable</td>\n",
       "      <td>600</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>软件服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>Nuro</td>\n",
       "      <td>575</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>机器人</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.0</td>\n",
       "      <td>Scale AI</td>\n",
       "      <td>490</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>人工智能</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.0</td>\n",
       "      <td>Weee</td>\n",
       "      <td>270</td>\n",
       "      <td>美国</td>\n",
       "      <td>菲蒙市</td>\n",
       "      <td>电子商务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.0</td>\n",
       "      <td>Workrise</td>\n",
       "      <td>190</td>\n",
       "      <td>美国</td>\n",
       "      <td>奥斯汀</td>\n",
       "      <td>电子商务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11.0</td>\n",
       "      <td>Binance.US</td>\n",
       "      <td>185</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>区块链</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12.0</td>\n",
       "      <td>Lime</td>\n",
       "      <td>155</td>\n",
       "      <td>美国</td>\n",
       "      <td>圣马特奥</td>\n",
       "      <td>共享经济</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13.0</td>\n",
       "      <td>Moveworks</td>\n",
       "      <td>140</td>\n",
       "      <td>美国</td>\n",
       "      <td>山景城</td>\n",
       "      <td>人工智能</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.0</td>\n",
       "      <td>Avant</td>\n",
       "      <td>135</td>\n",
       "      <td>美国</td>\n",
       "      <td>芝加哥</td>\n",
       "      <td>金融科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14.0</td>\n",
       "      <td>Sourcegraph</td>\n",
       "      <td>135</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>软件服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16.0</td>\n",
       "      <td>Thatgamecompany</td>\n",
       "      <td>130</td>\n",
       "      <td>美国</td>\n",
       "      <td>圣塔莫尼卡</td>\n",
       "      <td>游戏</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17.0</td>\n",
       "      <td>Optimism</td>\n",
       "      <td>110</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>区块链</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18.0</td>\n",
       "      <td>Hive</td>\n",
       "      <td>100</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>软件服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>18.0</td>\n",
       "      <td>Iterable</td>\n",
       "      <td>100</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>软件服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20.0</td>\n",
       "      <td>OPay</td>\n",
       "      <td>95</td>\n",
       "      <td>尼日利亚</td>\n",
       "      <td>伊凯贾</td>\n",
       "      <td>金融科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21.0</td>\n",
       "      <td>CaptivateIQ</td>\n",
       "      <td>80</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>软件服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>21.0</td>\n",
       "      <td>GrubMarket</td>\n",
       "      <td>80</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>快递</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23.0</td>\n",
       "      <td>Advance Intelligence Group</td>\n",
       "      <td>67</td>\n",
       "      <td>新加坡</td>\n",
       "      <td>新加坡</td>\n",
       "      <td>金融科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>23.0</td>\n",
       "      <td>Agile Robots</td>\n",
       "      <td>67</td>\n",
       "      <td>德国</td>\n",
       "      <td>吉尔兴</td>\n",
       "      <td>机器人</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>23.0</td>\n",
       "      <td>EcoFlow</td>\n",
       "      <td>67</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>新能源</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>23.0</td>\n",
       "      <td>Flash Express</td>\n",
       "      <td>67</td>\n",
       "      <td>泰国</td>\n",
       "      <td>曼谷</td>\n",
       "      <td>物流</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>23.0</td>\n",
       "      <td>GetYourGuide</td>\n",
       "      <td>67</td>\n",
       "      <td>德国</td>\n",
       "      <td>柏林</td>\n",
       "      <td>电子商务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>23.0</td>\n",
       "      <td>Human Interest</td>\n",
       "      <td>67</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>金融科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>23.0</td>\n",
       "      <td>JupiterOne</td>\n",
       "      <td>67</td>\n",
       "      <td>美国</td>\n",
       "      <td>Morrisville</td>\n",
       "      <td>网络安全</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>23.0</td>\n",
       "      <td>News Break</td>\n",
       "      <td>67</td>\n",
       "      <td>美国</td>\n",
       "      <td>山景城</td>\n",
       "      <td>传媒</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0                           1          2      3            4      5\n",
       "0    NaN                          企业  价值（亿元人民币）     国家           城市     行业\n",
       "1    1.0                          币安       3000    马耳他          马耳他    区块链\n",
       "2    2.0          Citadel Securities       1500     美国          芝加哥   金融科技\n",
       "3    3.0                        极兔速递       1300  印度尼西亚          雅加达   电子商务\n",
       "4    3.0                          极星       1300     瑞典          哥德堡  新能源汽车\n",
       "5    5.0                      Notion        670     美国          旧金山   软件服务\n",
       "6    6.0                    Airtable        600     美国          旧金山   软件服务\n",
       "7    7.0                        Nuro        575     美国          旧金山    机器人\n",
       "8    8.0                    Scale AI        490     美国          旧金山   人工智能\n",
       "9    9.0                        Weee        270     美国          菲蒙市   电子商务\n",
       "10  10.0                    Workrise        190     美国          奥斯汀   电子商务\n",
       "11  11.0                  Binance.US        185     美国          旧金山    区块链\n",
       "12  12.0                        Lime        155     美国         圣马特奥   共享经济\n",
       "13  13.0                   Moveworks        140     美国          山景城   人工智能\n",
       "14  14.0                       Avant        135     美国          芝加哥   金融科技\n",
       "15  14.0                 Sourcegraph        135     美国          旧金山   软件服务\n",
       "16  16.0             Thatgamecompany        130     美国        圣塔莫尼卡     游戏\n",
       "17  17.0                    Optimism        110     美国          旧金山    区块链\n",
       "18  18.0                        Hive        100     美国          旧金山   软件服务\n",
       "19  18.0                    Iterable        100     美国          旧金山   软件服务\n",
       "20  20.0                        OPay         95   尼日利亚          伊凯贾   金融科技\n",
       "21  21.0                 CaptivateIQ         80     美国          旧金山   软件服务\n",
       "22  21.0                  GrubMarket         80     美国          旧金山     快递\n",
       "23  23.0  Advance Intelligence Group         67    新加坡          新加坡   金融科技\n",
       "24  23.0                Agile Robots         67     德国          吉尔兴    机器人\n",
       "25  23.0                     EcoFlow         67     美国          旧金山    新能源\n",
       "26  23.0               Flash Express         67     泰国           曼谷     物流\n",
       "27  23.0                GetYourGuide         67     德国           柏林   电子商务\n",
       "28  23.0              Human Interest         67     美国          旧金山   金融科技\n",
       "29  23.0                  JupiterOne         67     美国  Morrisville   网络安全\n",
       "30  23.0                  News Break         67     美国          山景城     传媒"
      ]
     },
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